中国烟草学报2026，Vol.32Issue(3)：97-109,13.DOI:10.16472/j.chinatobacco.2025.T0293
基于WOA-LSSVM的烟叶生态区模式识别研究
Study on pattern recognition of tobacco ecological regions based on WOA-LSSVM
摘要
Abstract
To achieve rapid and accurate discrimination of tobacco aroma styles,166 typical tobacco samples in eight ecological regions were selected for analysis of the conventional physical indices and 70 chemical components of tobacco leaves.Redundant features were eliminated through Spearman correlation analysis,and key variables were selected by SVM-RFE.Then,a WOA-LSSVM model was constructed and compared with the LSSVM and SVM models optimized by PSO and GA.The results showed that:(1)Chemical components such as Amadori compounds,organic acids,reducing sugars,and starch significantly contributed to the identification of tobacco leaf aroma styles,and physical indicators such as leaf surface density and equilibrium moisture content could also assist in the determination;(2)WOA-LSSVM outperformed other models in classification performance(training set recognition accuracy was 100%,F1-score was 1.00;test set accuracy was 95.9%,F1-score was 0.92)and computational efficiency(training time was 12.1s);(3)Based on the selected physical and chemical indicators of flue-cured tobacco leaves,using WOA-LSSVM to establish a model can achieve rapid and accurate identification of the tobacco leaf ecological region.This study can provide new ideas for tobacco production area traceability and aroma type evaluation.关键词
鲸鱼算法/最小二乘支持向量机/烟叶/模式识别Key words
WOA/LSSVM/tobacco leaf/pattern recognition引用本文复制引用
陈蕊,吴昌健,伍鹏霖,孙培健,陈思蒙,曹毅,朱莹,孙学辉..基于WOA-LSSVM的烟叶生态区模式识别研究[J].中国烟草学报,2026,32(3):97-109,13.基金项目
中国烟草总公司重点研发项目"基于深度学习的卷烟燃烧数字化平台构建及应用研究"(No.110202202036) （No.110202202036）
江苏中烟工业有限责任公司科技项目"不同烟叶原料对卷烟燃烧特性的影响研究"(No.H202405) （No.H202405）
